meryembarkallah21/Machine-learning101

This repository containing various machine learning practice notebooks! 📚 These notebooks were created as part of small practical exercises during my school courses. Each notebook focuses on a different topic and covers different aspects of machine learning and data science.

Machine Learning Practice Notebooks 🤖

Welcome to my GitHub repository containing various machine learning practice notebooks! 📚 These notebooks were created as part of small practical exercises during my school courses. Each notebook focuses on a different topic and covers different aspects of machine learning and data science. Here's a brief overview of the notebooks in this repository:

List of Notebooks 📋

• machinelearning_ds.ipynb: This notebook covers loading and exploring datasets, data preprocessing, and building machine learning models using various algorithms. It includes topics like decision trees, random forests, support vector machines, and more.

• tp5_TP_ML_keras.ipynb: In this notebook, I delve into deep learning using Keras. It includes a practical implementation of a neural network for image classification using the MNIST dataset.

• tp4_kmeans.ipynb: This notebook explores the K-Means clustering algorithm. It includes an example of image segmentation using K-Means clustering.

• TP_Regression_final.ipynb: This notebook focuses on regression techniques in machine learning. It covers linear regression and showcases how to evaluate regression models.

• Tp2_ML.ipynb: In this notebook, I explore various preprocessing techniques and apply machine learning algorithms for classification tasks.

Getting Started 🚀

To get started with these notebooks, you can simply clone this repository to your local machine:

git clone https://github.com/your-username/machine-learning-practice.git

Make sure you have the required libraries installed by creating a virtual environment and installing the dependencies:

cd machine-learning-practice
python -m venv venv
source venv/bin/activate  # On Windows, use: venv\Scripts\activate
pip install -r requirements.txt

Now you can open and run the notebooks using Jupyter Notebook or JupyterLab.

License 📄

These notebooks are provided under the MIT License, which allows you to use and modify the code for your own learning purposes.

Feel free to explore the notebooks, experiment with the code, and adapt them to your own projects! If you find them helpful, don't forget to give this repository a ⭐️.

Happy learning and coding! 🎉😊